CANFIRE – Excel (W. deGroot)
Bill deGroot’s CANFIRE model remains useful for estimating certain fire effects such as fuel consumption and emissions. For some reason it is hard to find the desktop (Excel) version (a recent web search was fruitless), so I thought I would post it here. I was not involved in CANFIRE at all, so hopefully I’m not stepping on any toes by doing this.
C-7b Model (J. Beck)
Back when she was the Fire Science Officer for the BC Forest Service Protection Branch (BC Wildfire Service) and one of the instructors on the CIFFC Wildfire Behaviour Specialist (WFBS) course (numbered S-590 today), esteemed researcher and manager Judi Beck made a case for a version of C-7 that was sensitive to grass curing.
The point is that fire behaviour in open stands of ponderosa pine and Douglas-fir are highly sensitive to the degree of curing in the grass-herb understory, and when the grass is lush and green, fire spread is nearly impossible (as with pure grasslands with a low degree of curing); so Judi developed this calculator in order to adjust the curing in the C-7 (Ponderosa Pine-Douglas-fir) fuel type, the so-called C-7b. Judi originally developed this in 2003 but I updated it around 2015 to use the newer grass curing function, as described in the FBP ‘Updates and revisions’ report.
I still get requests for this calculator once in a while, so here it is. While I do have some concerns about the C-7b, this is not the place to get into them.
Original WFBS slides (Alexander/Beck)
New Grass Fire Model Prediction Tool (B.M. Wotton)
Over time, several concerns related to the FBP O-1a/b (‘Open’ fuel type – ie, grassland) models have emerged. One of the main ones is that the FFMC timelag is just too slow: grasslands (like other open fuel types completely exposed to the sun) react much more quickly than fuelbeds in the understory of a closed conifer stand. So the O-1 model might hold on to a high danger prediction when it has passed (such as if conditions cloud over and the RH increases) – a risk of overpredicting fire activity during low danger conditions.
Of much greater concern, however, is clear, sunny conditions, that quickly follow more humid or rainy conditions. In this case, direct sun and low humidity are rapidly reducing fuel moisture, while the FFMC traces a gradual increase to high danger conditions over a few days. But sun-baked grass fuels may actually be ready burn in just a few hours. This represents an underprediction potential, with volatile fuel conditions (e.g. moisture content < 10 %) occurring when the FFMC might only be 75 or 80, in the case of a day or two after rain.
Mike Wotton’s new grass fuel moisture model used a combination of physical principles and field data to come up with a model that is much more sensitive to changing hourly conditions (2009 conference paper). Also notable is that it was designed and tested in Ontario tallgrass prairies for prescribed fire, and there is a nice paper describing some additional experiments for this purpose by Kidnie and Wotton (2015).
While most of the new grass fuel moisture and fire behaviour system will eventually migrate over to the Next Generation-FBP System, in the meantime Mike has a handy spreadsheet tool for making calculations. The calculator allows users to make a midday or afternoon prediction based on starting indices and observed or forecast early day conditions.
Grass Fuel Moisture and Fire Behaviour Calculator, v2.1
FWI Multiday Calculator
This is for quickly calculating FWI codes and indices over several days, based on forecast weather and some starting codes. Ok, there are several other tools for this, notably the CFFDRS R package. But many FBANs aren’t going to dink around in R when deployed to an active fire incident. You can also do this in RedApp, but it’s clunky if you’re looking out several days in a row. This tool is for when you need a rapid calculator to enter custom weather observations and see the results immediately: varying rain gauge amounts, hypothetical wind speeds, fire behaviour reconstructions, and so on.
Note the different columns related to the two ISI measures. As the text specifies, they are the same at low wind speeds, but diverge above 40 km/h.
(I believe the original credit for this goes to Pat Payette who may have been working for Rick Arthur in Alberta, and it was part of the original ‘WFBS Spreadsheets’ file circulated around 2006. Since then I made a few small changes in 2014-2018, such as the ISI business described.)
McAlpine and Hobbs LCBH calculator
Back in the early 90s, when the FBP System was brand new, the fanciest fuel type of the bunch was the C-6 ‘Conifer plantation’. As I understand it, this was the Charlie Van Wagner special, the most sophisticated and flexible conifer fuel type, with a spread rate function that varied by foliar moisture content and by crown fraction burned as well. In order to use it, you were supposed to be dealing with conifer plantations, characterized by a single species, fairly uniform density and consistent live crown base height (LCBH).
How did one predict the LCBH? Well, McAlpine and Hobbs ran around the Petawawa, ON experimental forest and studied plantations of various boreal and sub-boreal species, and came up with a handy empirical model that makes quick work of predicting the LCBH.
Few people seem know about this work so there may be some interest in resurrecting it, but again, it may only really apply to plantations in the eastern Ontario region.
In any case, I made a simple spreadsheet calculator for those who would like to play around with it. The inputs are species, density and height, and really there’s not much point talking about it since it’s so simple. In many more sophisticated modelling efforts this will have been superseded by much fancier stand growth and fuel structure models, but sometimes it’s nice to have a quick way to estimate something important like LCBH.
McAlpine and Hobbs LCBH calculator
Stand-adjusted litter moisture model calculator (Wotton & Beverly)
After playing with this model a few years ago I wrote a little blurb about it here and included it in the ConPyro system models. I still like it. It has it’s quirks, but does a nice job incorporating a number of important variables from the user’s perspective. In my view, it’s a view of things to come in fire behaviour prediction and modelling as we gradually get more data and a more data-driven approach.
The calculator is pretty self-explanatory, so hopefully doesn’t give anyone too much trouble. This one is updated with a few more colours and labels but is otherwise the same as the previous spreadsheet tool.
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